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In theory, classic RNNs can keep track of arbitrary long-term dependencies in the input sequences. The problem with classic RNNs is computational (or practical) in nature: when training a classic RNN using back-propagation, the long-term gradients which are back-propagated can "vanish", meaning they can tend to zero due to very small numbers creeping into the computations, causing the model to ...
Like BERT (but unlike "bag of words" such as Word2Vec and GloVe), ELMo word embeddings are context-sensitive, producing different representations for words that share the same spelling. It was trained on a corpus of about 30 million sentences and 1 billion words. [4] Previously, bidirectional LSTM was used for contextualized word representation ...
Connectionist temporal classification (CTC) is a type of neural network output and associated scoring function, for training recurrent neural networks (RNNs) such as LSTM networks to tackle sequence problems where the timing is variable.
In language modelling, ELMo (2018) was a bi-directional LSTM that produces contextualized word embeddings, improving upon the line of research from bag of words and word2vec. It was followed by BERT (2018), an encoder-only Transformer model. [33] In 2019 October, Google started using BERT to process search queries. [34]
A demonic California dad has been arrested for allegedly beheading his 1-year-old son Friday in an early-morning frenzy of violence that also injured his wife and her mother, according to police.
The Pop-Tarts Bowl will have not one, not two, but three edible mascots this year. (Photo by David Rosenblum/Icon Sportswire via Getty Images) (Icon Sportswire via Getty Images)
A flight attendant has reportedly been injured after falling from a plane at an airport in England. On Monday, Dec. 16, at approximately 4:30 p.m. local time, the woman — who works for the ...
Removes the bias of subword tokenisation: where common subwords are overrepresented and rare or new words are underrepresented or split into less meaningful units. This can affect the model's understanding and generation capabilities, particularly for languages with rich morphology or tokens not well-represented in the training data.